search_space.add_budget(param.Budget.TIME_IN_H, 24) search_space.add_evaluation_metric(param.EvaluationMetric.MICRO_F1_SCORE) search_space.add_optimization_value(param.OptimizationValue.DEV_SCORE) search_space.add_max_epochs_per_training_run(25) search_space.add_parameter(param.SequenceTagger.HIDDEN_SIZE, options=[128, 256, 512]) search_space.add_parameter(param.SequenceTagger.DROPOUT, options=[0, 0.1, 0.2, 0.3]) search_space.add_parameter(param.SequenceTagger.WORD_DROPOUT, options=[0, 0.01, 0.05, 0.1]) search_space.add_parameter(param.SequenceTagger.RNN_LAYERS, options=[2, 3, 4, 5, 6]) search_space.add_parameter(param.SequenceTagger.USE_RNN, options=[True, False]) search_space.add_parameter(param.SequenceTagger.USE_CRF, options=[True, False]) search_space.add_parameter(param.SequenceTagger.REPROJECT_EMBEDDINGS, options=[True, False]) search_space.add_parameter( param.SequenceTagger.WORD_EMBEDDINGS, options=[WordEmbeddings('en'), ['en'], ['en', 'glove']]) search_strategy.make_configurations(search_space) orchestrator = orchestrator.Orchestrator( corpus=corpus, base_path="resources/evaluation_wnut_random", search_space=search_space, search_strategy=search_strategy) orchestrator.optimize()
search_space.add_parameter(param.DocumentRNNEmbeddings.HIDDEN_SIZE, options=[128, 256, 512]) search_space.add_parameter(param.DocumentRNNEmbeddings.DROPOUT, options=[0, 0.1, 0.2, 0.3, 0.4, 0.5]) search_space.add_parameter(param.DocumentRNNEmbeddings.REPROJECT_WORDS, options=[True, False]) search_space.add_parameter(param.DocumentRNNEmbeddings.WORD_EMBEDDINGS, options=[['glove'], ['en'], ['en', 'glove']]) #Define parameters for document embeddings Pool search_space.add_parameter(param.DocumentPoolEmbeddings.WORD_EMBEDDINGS, options=[['glove'], ['en'], ['en', 'glove']]) search_space.add_parameter(param.DocumentPoolEmbeddings.POOLING, options=['mean', 'max', 'min']) #Define parameters for Transformers search_space.add_parameter( param.TransformerDocumentEmbeddings.MODEL, options=["bert-base-uncased", "distilbert-base-uncased"]) search_space.add_parameter(param.TransformerDocumentEmbeddings.BATCH_SIZE, options=[16, 32, 64]) search_strategy.make_configurations(search_space) orchestrator = orchestrator.Orchestrator( corpus=corpus, base_path='resources/evaluation-senteval-subj-random', search_space=search_space, search_strategy=search_strategy) orchestrator.optimize()
search_space.add_parameter(param.DocumentRNNEmbeddings.HIDDEN_SIZE, options=[128, 256, 512]) search_space.add_parameter(param.DocumentRNNEmbeddings.DROPOUT, options=[0, 0.1, 0.2, 0.3, 0.4, 0.5]) search_space.add_parameter(param.DocumentRNNEmbeddings.REPROJECT_WORDS, options=[True, False]) search_space.add_parameter(param.DocumentRNNEmbeddings.WORD_EMBEDDINGS, options=[['glove'], ['en'], ['en', 'glove']]) #Define parameters for document embeddings Pool search_space.add_parameter(param.DocumentPoolEmbeddings.WORD_EMBEDDINGS, options=[['glove'], ['en'], ['en', 'glove']]) search_space.add_parameter(param.DocumentPoolEmbeddings.POOLING, options=['mean', 'max', 'min']) #Define parameters for Transformers search_space.add_parameter( param.TransformerDocumentEmbeddings.MODEL, options=["bert-base-uncased", "distilbert-base-uncased"]) search_space.add_parameter(param.TransformerDocumentEmbeddings.BATCH_SIZE, options=[16, 32, 64]) search_strategy.make_configurations(search_space) orchestrator = orchestrator.Orchestrator( corpus=corpus, base_path='resources/evaluation-trec-grid', search_space=search_space, search_strategy=search_strategy) orchestrator.optimize()
search_space.add_budget(param.Budget.TIME_IN_H, 24) search_space.add_evaluation_metric(param.EvaluationMetric.MICRO_F1_SCORE) search_space.add_optimization_value(param.OptimizationValue.DEV_SCORE) search_space.add_max_epochs_per_training_run(50) search_space.add_parameter(param.SequenceTagger.HIDDEN_SIZE, options=[128, 256, 512]) search_space.add_parameter(param.SequenceTagger.DROPOUT, options=[0, 0.1, 0.2, 0.3]) search_space.add_parameter(param.SequenceTagger.WORD_DROPOUT, options=[0, 0.01, 0.05, 0.1]) search_space.add_parameter(param.SequenceTagger.RNN_LAYERS, options=[2, 3, 4, 5, 6]) search_space.add_parameter(param.SequenceTagger.USE_RNN, options=[True, False]) search_space.add_parameter(param.SequenceTagger.USE_CRF, options=[True, False]) search_space.add_parameter(param.SequenceTagger.REPROJECT_EMBEDDINGS, options=[True, False]) search_space.add_parameter(param.SequenceTagger.WORD_EMBEDDINGS, options=[['glove'], ['en'], ['en', 'glove']]) search_strategy.make_configurations(search_space) orchestrator = orchestrator.Orchestrator( corpus=corpus, base_path="resources/evaluation_ud-eng_genetic-v2", search_space=search_space, search_strategy=search_strategy) orchestrator.optimize()
search_space.add_parameter(param.DocumentRNNEmbeddings.HIDDEN_SIZE, options=[128, 256, 512]) search_space.add_parameter(param.DocumentRNNEmbeddings.DROPOUT, options=[0, 0.1, 0.2, 0.3, 0.4, 0.5]) search_space.add_parameter(param.DocumentRNNEmbeddings.REPROJECT_WORDS, options=[True, False]) search_space.add_parameter(param.DocumentRNNEmbeddings.WORD_EMBEDDINGS, options=[['glove'], ['en'], ['en', 'glove']]) #Define parameters for document embeddings Pool search_space.add_parameter(param.DocumentPoolEmbeddings.WORD_EMBEDDINGS, options=[['glove'], ['en'], ['en', 'glove']]) search_space.add_parameter(param.DocumentPoolEmbeddings.POOLING, options=['mean', 'max', 'min']) #Define parameters for Transformers search_space.add_parameter( param.TransformerDocumentEmbeddings.MODEL, options=["bert-base-uncased", "distilbert-base-uncased"]) search_space.add_parameter(param.TransformerDocumentEmbeddings.BATCH_SIZE, options=[16, 32, 64]) search_strategy.make_configurations(search_space) orchestrator = orchestrator.Orchestrator( corpus=corpus, base_path='resources/evaluation-senteval-cr-genetic', search_space=search_space, search_strategy=search_strategy) orchestrator.optimize()